SAGMS decoder adapts Min-Sum scaling factor during decoding based on unsatisfied stabilizer fraction to match optimized scaled Min-Sum and approach BP performance for QLDPC codes at MS complexity.
Stabilizer codes and quantum error correction
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An ILP method yields new triorthogonal codes with prescribed dual distance not necessarily from triply-even classical codes, and a GRAND-based decoder performs well on high-distance instances over dephasing.
Extending affine subcode ensemble decoding to quantum codes with overcomplete matrices improves BP convergence and reduces logical error rates on toric and generalized bicycle codes.
Edge-coloring eliminates automorphisms in low-weight stabilizer subgraphs of generalized bicycle codes, enabling improved anisotropic min-sum decoding.
Proposes quantum sidecar architectures with stateful protected registers and stateless reset-reprepare modes as bounded signal generators for hybrid AI training and inference, supported by small-scale Qiskit simulations.
citing papers explorer
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Syndrome Adaptive Gain Control for Min-Sum Decoding of Quantum LDPC Codes
SAGMS decoder adapts Min-Sum scaling factor during decoding based on unsatisfied stabilizer fraction to match optimized scaled Min-Sum and approach BP performance for QLDPC codes at MS complexity.
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On Constructing and Decoding Quantum Triorthogonal Codes
An ILP method yields new triorthogonal codes with prescribed dual distance not necessarily from triply-even classical codes, and a GRAND-based decoder performs well on high-distance instances over dephasing.
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Affine Subcode Ensemble Decoding for Degeneracy-Aware Quantum Error Correction
Extending affine subcode ensemble decoding to quantum codes with overcomplete matrices improves BP convergence and reduces logical error rates on toric and generalized bicycle codes.
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Edge-Based Anisotropic Decoding for Generalized Bicycle Codes
Edge-coloring eliminates automorphisms in low-weight stabilizer subgraphs of generalized bicycle codes, enabling improved anisotropic min-sum decoding.
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Quantum Sidecar Architectures for Hybrid AI Training and Inference: Stateful Protected Registers, Stateless Reset-and-Reprepare Circuits and Quantum Weight-State Outlook
Proposes quantum sidecar architectures with stateful protected registers and stateless reset-reprepare modes as bounded signal generators for hybrid AI training and inference, supported by small-scale Qiskit simulations.